sustaining_gazes/matlab_version/face_validation/DeepLearnToolbox/NN/nnbp.m

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2016-04-28 21:40:36 +02:00
function nn = nnbp(nn)
%NNBP performs backpropagation
% nn = nnbp(nn) returns an neural network structure with updated weights
n = nn.n;
sparsityError = 0;
switch nn.output
case 'sigm'
d{n} = - nn.e .* (nn.a{n} .* (1 - nn.a{n}));
case {'softmax','linear'}
d{n} = - nn.e;
end
for i = (n - 1) : -1 : 2
% Derivative of the activation function
switch nn.activation_function
case 'sigm'
d_act = nn.a{i} .* (1 - nn.a{i});
case 'tanh_opt'
d_act = 1.7159 * 2/3 * (1 - 1/(1.7159)^2 * nn.a{i}.^2);
end
if(nn.nonSparsityPenalty>0)
pi = repmat(nn.p{i}, size(nn.a{i}, 1), 1);
sparsityError = [zeros(size(nn.a{i},1),1) nn.nonSparsityPenalty * (-nn.sparsityTarget ./ pi + (1 - nn.sparsityTarget) ./ (1 - pi))];
end
% Backpropagate first derivatives
if i+1==n % in this case in d{n} there is not the bias term to be removed
d{i} = (d{i + 1} * nn.W{i} + sparsityError) .* d_act; % Bishop (5.56)
else % in this case in d{i} the bias term has to be removed
d{i} = (d{i + 1}(:,2:end) * nn.W{i} + sparsityError) .* d_act;
end
if(nn.dropoutFraction>0)
d{i} = d{i} .* [ones(size(d{i},1),1) nn.dropOutMask{i}];
end
end
for i = 1 : (n - 1)
if i+1==n
nn.dW{i} = (d{i + 1}' * nn.a{i}) / size(d{i + 1}, 1);
else
nn.dW{i} = (d{i + 1}(:,2:end)' * nn.a{i}) / size(d{i + 1}, 1);
end
end
end